Terisa Roberts, Stephen J. Tonna

Risk Modeling

Practical Applications of Artificial Intelligence, Machine Learning, and Deep Learning

• Gebundenes Buch

Produktbeschreibung

A wide-ranging overview of the use of machine learning and AI techniques in financial risk management, including practical advice for implementation

Risk Modeling: Practical Applications of Artificial Intelligence, Machine Learning, and Deep Learning introduces readers to the use of innovative AI technologies for forecasting and evaluating financial risks. Providing up-to-date coverage of the practical application of current modelling techniques in risk management, this real-world guide also explores new opportunities and challenges associated with implementing machine learning and artificial intelligence (AI) into the risk management process.

Authors Terisa Roberts and Stephen Tonna provide readers with a clear understanding about the strengths and weaknesses of machine learning and AI while explaining how they can be applied to both everyday risk management problems and to evaluate the financial impact of extreme events such as global pandemics and changes in climate. Throughout the text, the authors clarify misconceptions about the use of machine learning and AI techniques using clear explanations while offering step-by-step advice for implementing the technologies into an organization's risk management model governance framework. This authoritative volume:
_ Highlights the use of machine learning and AI in identifying procedures for avoiding or minimizing financial risk
_ Discusses practical tools for assessing bias and interpretability of resultant models developed with machine learning algorithms and techniques
_ Covers the basic principles and nuances of feature engineering and common machine learning algorithms
_ Illustrates how risk modeling is incorporating machine learning and AI techniques to rapidly consume complex data and address current gaps in the end-to-end modelling lifecycle
_ Explains how proprietary software and open-source languages can be combined to deliver the best of both worlds: for risk models and risk practitioners

Risk Modeling: Practical Applications of Artificial Intelligence, Machine Learning, and Deep Learning is an invaluable guide for CEOs, CROs, CFOs, risk managers, business managers, and other professionals working in risk management.

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Produktdetails

• Wiley and SAS Business Series
• Verlag: Wiley / Wiley & Sons
• Artikelnr. des Verlages: 1W119824930
• 1. Auflage
• Seitenzahl: 208
• Erscheinungstermin: 27. September 2022
• Englisch
• Abmessung: 233mm x 160mm x 21mm
• Gewicht: 460g
• ISBN-13: 9781119824930
• ISBN-10: 1119824931
• Artikelnr.: 62228414

Autorenporträt

TERISA ROBERTS, PHD, is Global Solution Lead for Risk Modeling and Decisioning at SAS. She has nearly twenty years of experience in quantitative risk management and advanced analytics. She regularly advises banks and regulators around the world on industry best practices in AI, automation, and digitalization related to risk modeling and decisioning. STEPHEN J. TONNA, PHD, is a Senior Banking Solutions Advisor at SAS. He is a member of the SAS Risk Finance Advisory team for SAS Risk Research and Quantitative Solutions (RQS) in Asia Pacific. He received his doctorate in genetics, mathematics, and statistics from the University of Melbourne and research fellowship from the Brigham and Women's Hospital and Harvard Medical School.

Inhaltsangabe

Acknowledgments xi

Preface xiii

Chapter 1 Introduction 1

Risk Modeling: Definition and Brief History 4

Use of AI and Machine Learning in Risk Modeling 7

The New Risk Management Function 7

Overcoming Barriers to Technology and AI Adoption with a Little Help from Nature 10

This Book: What It Is and Is Not 11

Endnotes 12

Chapter 2 Data Management and Preparation 15

Importance of Data Governance to the Risk Function 18

Fundamentals of Data Management 20

Other Data Considerations for AI, Machine Learning, and Deep Learning 22

Concluding Remarks 29

Endnotes 30

Chapter 3 Artificial Intelligence, Machine Learning, and Deep Learning Models for Risk Management 31

Risk Modeling Using Machine Learning 35

Definitions of AI, Machine, and Deep Learning 40

Concluding Remarks 52

Endnotes 52

Chapter 4 Explaining Artificial Intelligence, Machine Learning, and Deep Learning Models 55

Difference Between Explaining and Interpreting Models 57

Why Explain AI Models 59

Common Approaches to Address Explainability of Data Used for Model Development 61

Common Approaches to Address Explainability of Models and Model Output 62

Limitations in Popular Methods 68

Concluding Remarks 69

Endnotes 69

Chapter 5 Bias, Fairness, and Vulnerability in Decision-Making 71

Assessing Bias in AI Systems 73

What Is Bias? 76

What Is Fairness? 77

Types of Bias in Decision-Making 78

Concluding Remarks 89

Endnotes 89

Chapter 6 Machine Learning Model Deployment, Implementation, and Making Decisions 91

Typical Model Deployment Challenges 93

Deployment Scenarios 98

Case Study: Enterprise Decisioning at a Global Bank 101

Practical Considerations 102

Model Orchestration 103

Concluding Remarks 104

Endnote 104

Chapter 7 Extending the Governance Framework for Machine Learning Validation and Ongoing Monitoring 105

Establishing the Right Internal Governance Framework 108

Developing Machine Learning Models with Governance in Mind 109

Monitoring AI and Machine Learning 112

Compliance Considerations 122

Further Takeaway 125

Concluding Remarks 126

Endnotes 127

Chapter 8 Optimizing Parameters for Machine Learning Models and Decisions in Production 129

Optimization for Machine Learning 131

Machine Learning Function Optimization Using Solvers 133

Tuning of Parameters 136

Other Optimization Algorithms for Risk Models 141

Machine Learning Models as Optimization Tools 143

Concluding Remarks 147

Endnotes 148

Chapter 9 The Interconnection between Climate and Financial Instability 149

Magnitude of Climate Instability: Understanding the "Why" of Climate Change Risk Management 152

Interconnected: Climate and Financial Stability 157

Assessing the impacts of climate change using AI and machine learning 158

Using scenario analysis to understand potential economic impact 160

Practical Examples 170

Concluding Remarks 172

Endnotes 172

About the Authors 175

Index 177